PMLE Automating and Orchestrating ML Pipelines Practice Question
What is the primary benefit of using pipeline caching in Vertex AI Pipelines?
⚠ Common exam trap
The exam often tests the distinction between caching (reusing outputs) and parallelization (running components concurrently), so candidates may confuse the two and incorrectly select parallel execution as the primary benefit.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
It reduces execution time and cost by reusing unchanged component outputs.
Pipeline caching in Vertex AI Pipelines automatically detects when a component's inputs and code have not changed from a previous execution and reuses the cached output artifacts. This avoids redundant computation, directly reducing both execution time and cost by skipping re-execution of unchanged steps.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
It reduces execution time and cost by reusing unchanged component outputs.
Why this is correct
Pipeline caching stores each component's outputs keyed by its inputs and code version, so unchanged steps skip re-execution entirely. This directly satisfies the stem's constraint of reducing execution time and cost, since Vertex AI Pipelines avoids recomputing identical artefacts and only reruns components whose inputs or definitions changed.
- ✗
It encrypts data at rest.
Why it's wrong here
Pipeline caching reuses outputs of previously executed components whose inputs and code are unchanged, skipping recomputation to cut cost and runtime. Encryption at rest is handled by the underlying Google Cloud storage and CMEK configuration, not by the cache mechanism. Caching would be chosen to avoid redundant step execution, not to protect stored artefacts.
- ✗
It automatically scales the pipeline resources.
Why it's wrong here
Pipeline caching reuses cached component outputs when inputs and code match, avoiding redundant execution to reduce cost and latency. Resource scaling is performed by Vertex AI's managed infrastructure and autoscaling settings, independent of caching. Caching would be the right choice when repeated pipeline runs share identical upstream steps.
- ✗
It enables parallel execution of components.
Why it's wrong here
Pipeline caching reuses outputs from prior identical component executions, skipping recomputation to save time and cost. Parallelism is achieved by defining components without data dependencies between them, letting Vertex AI schedule them concurrently. Caching would be correct when successive runs repeat unchanged steps, not when seeking concurrency.
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